The Genomics of Industrial Process Through the Qualia of Markovian Behavior

Abstract

A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system-state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the deoxyribonucleic acid, repeatable and predictable. The process genes are the quanta of information that aligns to represent a chain of activities (process). They describe the causal links between occurring events forming a pattern (pathway) that leads to a well-specified output (e.g., a product with a defect or otherwise). The creation of process genomics requires the knowledge of system observed or latent parameters (state) as well as the state change at specified time intervals (discretization). The process genomics theory is tested in an industrial case study for quality assessment and control of glue dispensing in micro-semiconductor manufacturing. The resulting definitions of the system state and interrelationship of control parameters contribute to the development of the process genes. The outcome of the gene alignment is the geometric interpretation of the glue droplet formation. A predicted or observed droplet within the production tolerance leads to a nondefective product. The principle of creating production genomics is to find and rectify the defect-causing genes or to disrupt the sequences that lead to producing defective products, leading to a zero-defect manufacturing process.

Industrial Genomics: A Novel Approach to System Behaviour Discovery

“…The concept of genomics, when applies to the field of the manufacturing process, refers to the comprehensive analysis of all parameters of a manufacturing process, their relationships, and their combined impact on the output of the manufacturing system. This concept is introduced by authors in [4]- [5] for manufacturing processes to reduce manufacturing defects and machine breakdowns by identifying defect-causing genes or disruptive sequences. We can rectify or remove these sequences from the process.…”

“…The Industrial Internet of Things (IIoT) revolution combination with today's advanced data analytics methods enables industries to implement new techniques for the purpose of manufacturing smartifications such as ZDM and PdM. This instigated the authors to present a novel approach for sequential learning of the Genomic of Industrial Process (GIP) in [4] and the Genomic of Machine Breakdown (GMB) in [5]. These studies introduced the theory of process and industrial genomics, inspired by biological genomics, to the field of manufacturing.…”